Systems and methods for deep brain stimulation using kinematic feedback for treatment of movement disorders

Abstract

Systems and methods for deep brain stimulation using kinematic feedback in accordance with embodiments of the invention are illustrated. One embodiment includes a deep brain stimulation system, including an implantable neurostimulator, a first inertial measurement unit (IMU), a second IMU, and a controller, where the controller is communicatively coupled to the implantable neurostimulator, the first IMU, and the second IMU, and where the controller is configured to obtain kinematic data from the first IMU and the second IMU, identify an abnormal movement event based on the kinematic data, and modify deep brain stimulation provided by the implantable neurostimulator based on the identified abnormal movement event.

What is claimed is: 
     
       1. A deep brain stimulation system, comprising:
 an implantable neurostimulator; 
 a first inertial measurement unit (IMU); 
 a second IMU; and 
 a controller, where the controller is communicatively coupled to the implantable neurostimulator, the first IMU, and the second IMU, and where the controller is configured to 
 obtain kinematic data from the first IMU and the second IMU; 
 predict a likelihood of a freezing of gait (FoG) event based on the kinematic data; and 
 modify deep brain stimulation provided by the implantable neurostimulator based on the predicted likelihood, wherein the modification comprises:
 decreasing stimulation intensity when the predicted likelihood is below a minimum threshold; 
 maintaining stimulation intensity when the predicted likelihood is between the minimum threshold and a maximum threshold; and 
 increasing stimulation intensity when the predicted likelihood is above the maximum threshold. 
 
 
     
     
       2. The deep brain stimulation system of  claim 1 , wherein the first IMU is configured to be attached to the right leg of a patient; and the second IMU is configured to be attached to the left leg of the patient. 
     
     
       3. The deep brain stimulation system of  claim 1 , wherein the kinematic data comprises a signal of angular momentum over time. 
     
     
       4. The deep brain stimulation system of  claim 1 , wherein in order to predict a likelihood of the FoG event, the controller is further configured to:
 calculate metrics comprising: arrhythmicity over a most recent set of steps, stride time, swing angular range, and asymmetry over the most recent set of steps; and 
 provide the calculated metrics to a logistic regression model to obtain a probability of the FOG event occurring. 
 
     
     
       5. The deep brain stimulation system of  claim 1 , further comprising modifying the deep brain stimulation according to a stimulation map, wherein the stimulation the stimulation map comprises the following changes in stimulation parameters:
 ramp stimulation frequency to 140 Hz when the predicted likelihood is below a minimum threshold; 
 maintain stimulation frequency when the predicted likelihood is between the minimum threshold and a maximum threshold; and 
 ramp stimulation frequency to 60 Hz when the predicted likelihood is above the maximum threshold. 
 
     
     
       6. The deep brain stimulation system of  claim 5 , wherein the minimum threshold is 30%, and the maximum threshold is 70%. 
     
     
       7. A method for deep brain stimulation, comprising:
 obtaining kinematic data from a first inertial measurement unit (IMU) and a second IMU using a controller communicatively coupled to the first IMU and the second IMU; 
 predict a likelihood of a freezing of gait (FoG) event based on the kinematic data using the controller; and 
 modifying deep brain stimulation provided by an implantable neurostimulator communicatively coupled with the controller based on the predicted likelihood, wherein the modification comprises:
 decreasing stimulation intensity when the predicted likelihood is below a minimum threshold; 
 maintaining stimulation intensity when the predicted likelihood is between the minimum threshold and a maximum threshold; and 
 increasing stimulation intensity when the predicted likelihood is above the maximum threshold. 
 
 
     
     
       8. The method of deep brain stimulation of  claim 7 , wherein the first IMU is configured to be attached to the right leg of a patient; and the second IMU is configured to be attached to the left leg of the patient. 
     
     
       9. The method of deep brain stimulation of  claim 7 , wherein the kinematic data comprises a signal of angular momentum over time. 
     
     
       10. The method of deep brain stimulation of  claim 7 , wherein predicting the likelihood of the FoG event comprises:
 calculating metrics comprising: arrhythmicity over a most recent set of steps, stride time, swing angular range, and asymmetry over the most recent set of steps; and 
 providing the calculated metrics to a logistic regression model to obtain a probability of the FOG event occurring. 
 
     
     
       11. The method of deep brain stimulation of  claim 7 , further comprising modifying the deep brain stimulation according to a stimulation map, wherein the stimulation map comprises the following changes in stimulation parameters:
 ramp stimulation frequency to 140 Hz when the predicted likelihood is below a minimum threshold; 
 maintain stimulation frequency when the predicted likelihood is between the minimum threshold and a maximum threshold; and 
 ramp stimulation frequency to 60 Hz when the predicted likelihood is above the maximum threshold. 
 
     
     
       12. The method of deep brain stimulation of  claim 11 , wherein the minimum threshold is 30%, and the maximum threshold is 70%.